This module implements a simple E-matching procedure as a backtracking search.

inductive Lean.Meta.Grind.EMatch.Cnstr : Type

We represent an E-matching problem as a list of constraints.

• match (gen? : Option GenPatternInfo) (pat e : Expr) : Cnstr

  Matches pattern pat with term e

• offset (gen? : Option GenPatternInfo) (pat : Expr) (k : Nat) (e : Expr) : Cnstr

  Matches offset pattern pat+k with term e

• continue (pat : Expr) : Cnstr

  This constraint is used to encode multi-patterns.

instance Lean.Meta.Grind.EMatch.instInhabitedCnstr : Inhabited Cnstr

Equations

• Lean.Meta.Grind.EMatch.instInhabitedCnstr = { default := Lean.Meta.Grind.EMatch.Cnstr.match default default default }

structure Lean.Meta.Grind.EMatch.Choice : Type

Choice point for the backtracking search. The state of the procedure contains a stack of choices.

• cnstrs : List Cnstr

  Constraints to be processed.

• gen : Nat

  Maximum term generation found so far.

• assignment : Array Expr

  Partial assignment so far. Recall that pattern variables are encoded as de-Bruijn variables.

instance Lean.Meta.Grind.EMatch.instInhabitedChoice : Inhabited Choice

Equations

• Lean.Meta.Grind.EMatch.instInhabitedChoice = { default := { cnstrs := default, gen := default, assignment := default } }

structure Lean.Meta.Grind.EMatch.Context : Type

Context for the E-matching monad.

• useMT : Bool

  useMT is true if we are using the mod-time optimization. It is always set to false for new EMatchTheorems.

• thm : EMatchTheorem

  EMatchTheorem being processed.

• initApp : Expr

  Initial application used to start E-matching

instance Lean.Meta.Grind.EMatch.instInhabitedContext : Inhabited Context

Equations

• Lean.Meta.Grind.EMatch.instInhabitedContext = { default := { useMT := default } }

structure Lean.Meta.Grind.EMatch.SearchState : Type

State for the E-matching monad

• choiceStack : List Choice

  Choices that still have to be processed.

instance Lean.Meta.Grind.EMatch.instInhabitedSearchState : Inhabited SearchState

Equations

• Lean.Meta.Grind.EMatch.instInhabitedSearchState = { default := { choiceStack := default } }

@[reducible, inline]

abbrev Lean.Meta.Grind.EMatch.M (α : Type) : Type

Equations

• Lean.Meta.Grind.EMatch.M = ReaderT Lean.Meta.Grind.EMatch.Context (StateRefT' IO.RealWorld Lean.Meta.Grind.EMatch.SearchState Lean.Meta.Grind.GoalM)

def Lean.Meta.Grind.EMatch.M.run' {α : Type} (x : M α) : GoalM α

Equations

• x.run' = (x { }).run' { }

def Lean.Meta.Grind.GenPatternInfo.assign? (genInfo : GenPatternInfo) (c : EMatch.Choice) (x : Expr) : OptionT GoalM EMatch.Choice

Equations

• genInfo.assign? c x = do let c ← Lean.Meta.Grind.EMatch.assign?✝ c genInfo.xIdx x let c ← Lean.Meta.Grind.EMatch.assignDelayedEqProof?✝ c genInfo.hIdx pure c

def Lean.Meta.Grind.EMatch.ematchTheorem (thm : EMatchTheorem) : M Unit

Equations

• One or more equations did not get rendered due to their size.

def Lean.Meta.Grind.EMatch.ematchTheorem.tryAll (ps : List Expr) (cs : List Cnstr) : M Unit

When using the mod-time optimization with multi-patterns, we must start ematching at each different pattern. That is, if we have [p₁, p₂, p₃], we must execute

• main p₁ [.continue p₂, .continue p₃]
• main p₂ [.continue p₁, .continue p₃]
• main p₃ [.continue p₁, .continue p₂]

Equations

• One or more equations did not get rendered due to their size.
• Lean.Meta.Grind.EMatch.ematchTheorem.tryAll [] cs = pure ()

def Lean.Meta.Grind.EMatch.ematchTheorems (thms : PArray EMatchTheorem) : M Unit

Equations

• Lean.Meta.Grind.EMatch.ematchTheorems thms = Lean.PersistentArray.forM thms Lean.Meta.Grind.EMatch.ematchTheorem

def Lean.Meta.Grind.ematch : GoalM Bool

Performs one round of E-matching, and returns true if new instances were generated.

Equations

• One or more equations did not get rendered due to their size.